Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations280,140
Missing cells2,322,928
Missing cells (%)39.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory68.6 MiB
Average record size in memory256.9 B

Variable types

Numeric6
Categorical12
DateTime2
Text1

Alerts

Nationality has a high cardinality: 101 distinct values High cardinality
SubDist has a high cardinality: 5485 distinct values High cardinality
District has a high cardinality: 990 distinct values High cardinality
Province has a high cardinality: 155 distinct values High cardinality
AccSubDist has a high cardinality: 5967 distinct values High cardinality
AccDistrict has a high cardinality: 1117 distinct values High cardinality
AccProvince has a high cardinality: 78 distinct values High cardinality
ICD_10 has a high cardinality: 375 distinct values High cardinality
AccLong is highly overall correlated with RiskSafetyBeltHigh correlation
AccProvince is highly overall correlated with RiskSafetyBeltHigh correlation
DeadYear_AD is highly overall correlated with DeadYear_BE and 1 other fieldsHigh correlation
DeadYear_BE is highly overall correlated with DeadYear_AD and 1 other fieldsHigh correlation
RiskHelmet is highly overall correlated with idHigh correlation
RiskSafetyBelt is highly overall correlated with AccLong and 2 other fieldsHigh correlation
id is highly overall correlated with DeadYear_AD and 3 other fieldsHigh correlation
Nationality is highly imbalanced (90.2%) Imbalance
ICD_10 is highly imbalanced (60.4%) Imbalance
Age has 30261 (10.8%) missing values Missing
Nationality has 133489 (47.7%) missing values Missing
SubDist has 245882 (87.8%) missing values Missing
District has 178554 (63.7%) missing values Missing
Province has 178372 (63.7%) missing values Missing
RiskHelmet has 275750 (98.4%) missing values Missing
RiskSafetyBelt has 278074 (99.3%) missing values Missing
DateRec has 173190 (61.8%) missing values Missing
TimeRec has 173190 (61.8%) missing values Missing
AccSubDist has 150422 (53.7%) missing values Missing
AccDistrict has 137688 (49.1%) missing values Missing
AccLat has 154275 (55.1%) missing values Missing
AccLong has 154276 (55.1%) missing values Missing
ICD_10 has 59503 (21.2%) missing values Missing
id has unique values Unique

Reproduction

Analysis started2025-07-17 15:26:11.978011
Analysis finished2025-07-17 15:28:10.857639
Duration1 minute and 58.88 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Unique 

Distinct280140
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9687850.9
Minimum2999564
Maximum11683229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-07-17T22:28:11.183626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2999564
5-th percentile8604536.9
Q18662483.8
median9615955.5
Q310928280
95-th percentile11669222
Maximum11683229
Range8683665
Interquartile range (IQR)2265796.5

Descriptive statistics

Standard deviation1452799.6
Coefficient of variation (CV)0.14996098
Kurtosis4.5877139
Mean9687850.9
Median Absolute Deviation (MAD)970580
Skewness-1.0866984
Sum2.7139546 × 1012
Variance2.1106267 × 1012
MonotonicityNot monotonic
2025-07-17T22:28:11.630540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3004927 1
 
< 0.1%
8600819 1
 
< 0.1%
8600823 1
 
< 0.1%
8600822 1
 
< 0.1%
8610778 1
 
< 0.1%
8608938 1
 
< 0.1%
8606732 1
 
< 0.1%
8607223 1
 
< 0.1%
8604797 1
 
< 0.1%
8600820 1
 
< 0.1%
Other values (280130) 280130
> 99.9%
ValueCountFrequency (%)
2999564 1
< 0.1%
2999565 1
< 0.1%
2999566 1
< 0.1%
2999568 1
< 0.1%
2999569 1
< 0.1%
2999570 1
< 0.1%
2999574 1
< 0.1%
2999576 1
< 0.1%
2999577 1
< 0.1%
2999578 1
< 0.1%
ValueCountFrequency (%)
11683229 1
< 0.1%
11683228 1
< 0.1%
11683227 1
< 0.1%
11683226 1
< 0.1%
11683225 1
< 0.1%
11683224 1
< 0.1%
11683223 1
< 0.1%
11683222 1
< 0.1%
11683221 1
< 0.1%
11683220 1
< 0.1%

DeadYear_BE
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2560.2866
Minimum2554
Maximum2568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-07-17T22:28:11.923607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2554
5-th percentile2554
Q12557
median2560
Q32564
95-th percentile2567
Maximum2568
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.0788523
Coefficient of variation (CV)0.0015931233
Kurtosis-1.1489513
Mean2560.2866
Median Absolute Deviation (MAD)3
Skewness0.12082727
Sum7.172387 × 108
Variance16.637036
MonotonicityIncreasing
2025-07-17T22:28:12.247186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2554 21996
 
7.9%
2559 21745
 
7.8%
2560 21607
 
7.7%
2555 21603
 
7.7%
2556 21221
 
7.6%
2557 20790
 
7.4%
2558 19960
 
7.1%
2561 19931
 
7.1%
2562 19904
 
7.1%
2563 17831
 
6.4%
Other values (5) 73552
26.3%
ValueCountFrequency (%)
2554 21996
7.9%
2555 21603
7.7%
2556 21221
7.6%
2557 20790
7.4%
2558 19960
7.1%
2559 21745
7.8%
2560 21607
7.7%
2561 19931
7.1%
2562 19904
7.1%
2563 17831
6.4%
ValueCountFrequency (%)
2568 4241
 
1.5%
2567 17477
6.2%
2566 17498
6.2%
2565 17379
6.2%
2564 16957
6.1%
2563 17831
6.4%
2562 19904
7.1%
2561 19931
7.1%
2560 21607
7.7%
2559 21745
7.8%

DeadYear_AD
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.2866
Minimum2011
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-07-17T22:28:12.534210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12014
median2017
Q32021
95-th percentile2024
Maximum2025
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.0788523
Coefficient of variation (CV)0.0020219498
Kurtosis-1.1489513
Mean2017.2866
Median Absolute Deviation (MAD)3
Skewness0.12082727
Sum5.6512268 × 108
Variance16.637036
MonotonicityIncreasing
2025-07-17T22:28:12.825877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2011 21996
 
7.9%
2016 21745
 
7.8%
2017 21607
 
7.7%
2012 21603
 
7.7%
2013 21221
 
7.6%
2014 20790
 
7.4%
2015 19960
 
7.1%
2018 19931
 
7.1%
2019 19904
 
7.1%
2020 17831
 
6.4%
Other values (5) 73552
26.3%
ValueCountFrequency (%)
2011 21996
7.9%
2012 21603
7.7%
2013 21221
7.6%
2014 20790
7.4%
2015 19960
7.1%
2016 21745
7.8%
2017 21607
7.7%
2018 19931
7.1%
2019 19904
7.1%
2020 17831
6.4%
ValueCountFrequency (%)
2025 4241
 
1.5%
2024 17477
6.2%
2023 17498
6.2%
2022 17379
6.2%
2021 16957
6.1%
2020 17831
6.4%
2019 19904
7.1%
2018 19931
7.1%
2017 21607
7.7%
2016 21745
7.8%

Age
Real number (ℝ)

Missing 

Distinct118
Distinct (%)< 0.1%
Missing30261
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean40.520904
Minimum-22
Maximum154
Zeros1864
Zeros (%)0.7%
Negative6
Negative (%)< 0.1%
Memory size4.5 MiB
2025-07-17T22:28:13.311355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-22
5-th percentile14
Q124
median39
Q355
95-th percentile74
Maximum154
Range176
Interquartile range (IQR)31

Descriptive statistics

Standard deviation19.892072
Coefficient of variation (CV)0.49090888
Kurtosis-0.11695552
Mean40.520904
Median Absolute Deviation (MAD)16
Skewness0.39876005
Sum10125323
Variance395.69451
MonotonicityNot monotonic
2025-07-17T22:28:13.888945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 5881
 
2.1%
18 5689
 
2.0%
17 5619
 
2.0%
20 5578
 
2.0%
16 5252
 
1.9%
21 5195
 
1.9%
23 5127
 
1.8%
22 4931
 
1.8%
24 4841
 
1.7%
15 4709
 
1.7%
Other values (108) 197057
70.3%
(Missing) 30261
 
10.8%
ValueCountFrequency (%)
-22 1
 
< 0.1%
-1 5
 
< 0.1%
0 1864
0.7%
1 687
 
0.2%
2 543
 
0.2%
3 467
 
0.2%
4 473
 
0.2%
5 477
 
0.2%
6 446
 
0.2%
7 373
 
0.1%
ValueCountFrequency (%)
154 1
 
< 0.1%
149 19
 
< 0.1%
131 2
 
< 0.1%
130 438
0.2%
129 48
 
< 0.1%
128 2
 
< 0.1%
125 2
 
< 0.1%
123 1
 
< 0.1%
122 1
 
< 0.1%
119 2
 
< 0.1%

Sex
Categorical

Distinct3
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size2.4 MiB
ชาย
214236 
หญิง
59338 
ไม่ระบุ
 
6564

Length

Max length7
Median length3
Mean length3.3055423
Min length3

Characters and Unicode

Total characters926,008
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowชาย
2nd rowชาย
3rd rowชาย
4th rowไม่ระบุ
5th rowไม่ระบุ

Common Values

ValueCountFrequency (%)
ชาย 214236
76.5%
หญิง 59338
 
21.2%
ไม่ระบุ 6564
 
2.3%
(Missing) 2
 
< 0.1%

Length

2025-07-17T22:28:14.285682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T22:28:14.548655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ชาย 214236
76.5%
หญิง 59338
 
21.2%
ไม่ระบุ 6564
 
2.3%

Most occurring characters

ValueCountFrequency (%)
214236
23.1%
214236
23.1%
214236
23.1%
59338
 
6.4%
59338
 
6.4%
59338
 
6.4%
59338
 
6.4%
6564
 
0.7%
6564
 
0.7%
6564
 
0.7%
Other values (4) 26256
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 926008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
214236
23.1%
214236
23.1%
214236
23.1%
59338
 
6.4%
59338
 
6.4%
59338
 
6.4%
59338
 
6.4%
6564
 
0.7%
6564
 
0.7%
6564
 
0.7%
Other values (4) 26256
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 926008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
214236
23.1%
214236
23.1%
214236
23.1%
59338
 
6.4%
59338
 
6.4%
59338
 
6.4%
59338
 
6.4%
6564
 
0.7%
6564
 
0.7%
6564
 
0.7%
Other values (4) 26256
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 926008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
214236
23.1%
214236
23.1%
214236
23.1%
59338
 
6.4%
59338
 
6.4%
59338
 
6.4%
59338
 
6.4%
6564
 
0.7%
6564
 
0.7%
6564
 
0.7%
Other values (4) 26256
 
2.8%

Nationality
Categorical

High cardinality  Imbalance  Missing 

Distinct101
Distinct (%)0.1%
Missing133489
Missing (%)47.7%
Memory size2.4 MiB
99.0
126161 
Thai
19417 
Burmese
 
517
Cambodian
 
81
Lao, Laotian
 
39
Other values (96)
 
436

Length

Max length21
Median length4
Mean length4.0255027
Min length3

Characters and Unicode

Total characters590,344
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st row99.0
2nd row99.0
3rd row99.0
4th row99.0
5th row99.0

Common Values

ValueCountFrequency (%)
99.0 126161
45.0%
Thai 19417
 
6.9%
Burmese 517
 
0.2%
Cambodian 81
 
< 0.1%
Lao, Laotian 39
 
< 0.1%
Beninese 38
 
< 0.1%
198.0 22
 
< 0.1%
44.0 21
 
< 0.1%
Lao Laotian 17
 
< 0.1%
Russian 15
 
< 0.1%
Other values (91) 323
 
0.1%
(Missing) 133489
47.7%

Length

2025-07-17T22:28:14.947229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
99.0 126161
86.0%
thai 19417
 
13.2%
burmese 517
 
0.4%
cambodian 81
 
0.1%
lao 56
 
< 0.1%
laotian 56
 
< 0.1%
beninese 38
 
< 0.1%
198.0 22
 
< 0.1%
44.0 21
 
< 0.1%
russian 15
 
< 0.1%
Other values (98) 334
 
0.2%

Most occurring characters

ValueCountFrequency (%)
9 252371
42.7%
0 126293
21.4%
. 126287
21.4%
a 20047
 
3.4%
i 19839
 
3.4%
h 19506
 
3.3%
T 19420
 
3.3%
e 1304
 
0.2%
s 672
 
0.1%
m 659
 
0.1%
Other values (50) 3946
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 590344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 252371
42.7%
0 126293
21.4%
. 126287
21.4%
a 20047
 
3.4%
i 19839
 
3.4%
h 19506
 
3.3%
T 19420
 
3.3%
e 1304
 
0.2%
s 672
 
0.1%
m 659
 
0.1%
Other values (50) 3946
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 590344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 252371
42.7%
0 126293
21.4%
. 126287
21.4%
a 20047
 
3.4%
i 19839
 
3.4%
h 19506
 
3.3%
T 19420
 
3.3%
e 1304
 
0.2%
s 672
 
0.1%
m 659
 
0.1%
Other values (50) 3946
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 590344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 252371
42.7%
0 126293
21.4%
. 126287
21.4%
a 20047
 
3.4%
i 19839
 
3.4%
h 19506
 
3.3%
T 19420
 
3.3%
e 1304
 
0.2%
s 672
 
0.1%
m 659
 
0.1%
Other values (50) 3946
 
0.7%

SubDist
Categorical

High cardinality  Missing 

Distinct5485
Distinct (%)16.0%
Missing245882
Missing (%)87.8%
Memory size3.3 MiB
ในเมือง
 
332
หนองบัว
 
121
หนองปรือ
 
92
หนองแวง
 
72
วังทอง
 
65
Other values (5480)
33576 

Length

Max length20
Median length18
Mean length7.349495
Min length2

Characters and Unicode

Total characters251,779
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique698 ?
Unique (%)2.0%

Sample

1st rowเขาพระงาม
2nd rowบ้านเป้า
3rd rowชีวึก
4th rowบ้านแก้ง
5th rowสันนาเม็ง

Common Values

ValueCountFrequency (%)
ในเมือง 332
 
0.1%
หนองบัว 121
 
< 0.1%
หนองปรือ 92
 
< 0.1%
หนองแวง 72
 
< 0.1%
วังทอง 65
 
< 0.1%
เวียง 61
 
< 0.1%
ท่าข้าม 61
 
< 0.1%
ปากน้ำ 58
 
< 0.1%
หน้าเมือง 54
 
< 0.1%
สระแก้ว 54
 
< 0.1%
Other values (5475) 33288
 
11.9%
(Missing) 245882
87.8%

Length

2025-07-17T22:28:15.897355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ในเมือง 332
 
1.0%
หนองบัว 121
 
0.4%
หนองปรือ 92
 
0.3%
หนองแวง 72
 
0.2%
วังทอง 65
 
0.2%
ท่าข้าม 61
 
0.2%
เวียง 61
 
0.2%
ปากน้ำ 58
 
0.2%
สระแก้ว 54
 
0.2%
หน้าเมือง 54
 
0.2%
Other values (5475) 33292
97.2%

Most occurring characters

ValueCountFrequency (%)
22612
 
9.0%
18982
 
7.5%
18124
 
7.2%
12546
 
5.0%
9290
 
3.7%
9067
 
3.6%
8824
 
3.5%
8809
 
3.5%
8655
 
3.4%
8525
 
3.4%
Other values (59) 126345
50.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 251779
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22612
 
9.0%
18982
 
7.5%
18124
 
7.2%
12546
 
5.0%
9290
 
3.7%
9067
 
3.6%
8824
 
3.5%
8809
 
3.5%
8655
 
3.4%
8525
 
3.4%
Other values (59) 126345
50.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 251779
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22612
 
9.0%
18982
 
7.5%
18124
 
7.2%
12546
 
5.0%
9290
 
3.7%
9067
 
3.6%
8824
 
3.5%
8809
 
3.5%
8655
 
3.4%
8525
 
3.4%
Other values (59) 126345
50.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 251779
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22612
 
9.0%
18982
 
7.5%
18124
 
7.2%
12546
 
5.0%
9290
 
3.7%
9067
 
3.6%
8824
 
3.5%
8809
 
3.5%
8655
 
3.4%
8525
 
3.4%
Other values (59) 126345
50.2%

District
Categorical

High cardinality  Missing 

Distinct990
Distinct (%)1.0%
Missing178554
Missing (%)63.7%
Memory size2.8 MiB
เมือง
 
628
เมืองอุดรธานี
 
616
เมืองนครราชสีมา
 
571
เมืองขอนแก่น
 
560
เมืองสมุทรปราการ
 
556
Other values (985)
98655 

Length

Max length22
Median length18
Mean length8.5569862
Min length2

Characters and Unicode

Total characters869,270
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowเมืองลพบุรี
2nd rowหนองสูง
3rd rowขามสะแกแสง
4th rowนาแก
5th rowสันทราย

Common Values

ValueCountFrequency (%)
เมือง 628
 
0.2%
เมืองอุดรธานี 616
 
0.2%
เมืองนครราชสีมา 571
 
0.2%
เมืองขอนแก่น 560
 
0.2%
เมืองสมุทรปราการ 556
 
0.2%
เมืองนครปฐม 522
 
0.2%
เมืองลพบุรี 513
 
0.2%
เมืองเชียงราย 508
 
0.2%
เมืองเพชรบูรณ์ 492
 
0.2%
เมืองลำปาง 486
 
0.2%
Other values (980) 96134
34.3%
(Missing) 178554
63.7%

Length

2025-07-17T22:28:16.708246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
กิ่ง 681
 
0.7%
เมือง 628
 
0.6%
เมืองอุดรธานี 616
 
0.6%
เมืองนครราชสีมา 571
 
0.6%
เมืองขอนแก่น 560
 
0.5%
เมืองสมุทรปราการ 556
 
0.5%
เมืองนครปฐม 522
 
0.5%
เมืองลพบุรี 513
 
0.5%
เมืองเชียงราย 508
 
0.5%
เมืองเพชรบูรณ์ 492
 
0.5%
Other values (975) 96749
94.5%

Most occurring characters

ValueCountFrequency (%)
66466
 
7.6%
63887
 
7.3%
54877
 
6.3%
48336
 
5.6%
48066
 
5.5%
47367
 
5.4%
40710
 
4.7%
25724
 
3.0%
24462
 
2.8%
24212
 
2.8%
Other values (56) 425163
48.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 869270
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
66466
 
7.6%
63887
 
7.3%
54877
 
6.3%
48336
 
5.6%
48066
 
5.5%
47367
 
5.4%
40710
 
4.7%
25724
 
3.0%
24462
 
2.8%
24212
 
2.8%
Other values (56) 425163
48.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 869270
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
66466
 
7.6%
63887
 
7.3%
54877
 
6.3%
48336
 
5.6%
48066
 
5.5%
47367
 
5.4%
40710
 
4.7%
25724
 
3.0%
24462
 
2.8%
24212
 
2.8%
Other values (56) 425163
48.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 869270
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
66466
 
7.6%
63887
 
7.3%
54877
 
6.3%
48336
 
5.6%
48066
 
5.5%
47367
 
5.4%
40710
 
4.7%
25724
 
3.0%
24462
 
2.8%
24212
 
2.8%
Other values (56) 425163
48.9%

Province
Categorical

High cardinality  Missing 

Distinct155
Distinct (%)0.2%
Missing178372
Missing (%)63.7%
Memory size2.7 MiB
กรุงเทพมหานคร
 
2245
นครราชสีมา
 
2047
กท
 
2029
นม
 
2001
เชียงใหม่
 
1578
Other values (150)
91868 

Length

Max length15
Median length13
Mean length5.0666516
Min length2

Characters and Unicode

Total characters515,623
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowลบ
2nd rowมห
3rd rowนม
4th rowนพ
5th rowชม

Common Values

ValueCountFrequency (%)
กรุงเทพมหานคร 2245
 
0.8%
นครราชสีมา 2047
 
0.7%
กท 2029
 
0.7%
นม 2001
 
0.7%
เชียงใหม่ 1578
 
0.6%
ชม 1526
 
0.5%
ขก 1321
 
0.5%
อุบลราชธานี 1304
 
0.5%
เชียงราย 1301
 
0.5%
อุดรธานี 1295
 
0.5%
Other values (145) 85121
30.4%
(Missing) 178372
63.7%

Length

2025-07-17T22:28:17.277353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
กรุงเทพมหานคร 2245
 
2.2%
นครราชสีมา 2047
 
2.0%
กท 2029
 
2.0%
นม 2001
 
2.0%
เชียงใหม่ 1578
 
1.6%
ชม 1526
 
1.5%
ขก 1321
 
1.3%
อุบลราชธานี 1304
 
1.3%
เชียงราย 1301
 
1.3%
อุดรธานี 1295
 
1.3%
Other values (145) 85121
83.6%

Most occurring characters

ValueCountFrequency (%)
65064
 
12.6%
37262
 
7.2%
30238
 
5.9%
24578
 
4.8%
23030
 
4.5%
22537
 
4.4%
20551
 
4.0%
20441
 
4.0%
20340
 
3.9%
19579
 
3.8%
Other values (42) 232003
45.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 515623
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
65064
 
12.6%
37262
 
7.2%
30238
 
5.9%
24578
 
4.8%
23030
 
4.5%
22537
 
4.4%
20551
 
4.0%
20441
 
4.0%
20340
 
3.9%
19579
 
3.8%
Other values (42) 232003
45.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 515623
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
65064
 
12.6%
37262
 
7.2%
30238
 
5.9%
24578
 
4.8%
23030
 
4.5%
22537
 
4.4%
20551
 
4.0%
20441
 
4.0%
20340
 
3.9%
19579
 
3.8%
Other values (42) 232003
45.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 515623
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
65064
 
12.6%
37262
 
7.2%
30238
 
5.9%
24578
 
4.8%
23030
 
4.5%
22537
 
4.4%
20551
 
4.0%
20441
 
4.0%
20340
 
3.9%
19579
 
3.8%
Other values (42) 232003
45.0%

RiskHelmet
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.1%
Missing275750
Missing (%)98.4%
Memory size19.2 MiB
2
3048 
1
1121 
3
 
221

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4,390
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row3
5th row1

Common Values

ValueCountFrequency (%)
2 3048
 
1.1%
1 1121
 
0.4%
3 221
 
0.1%
(Missing) 275750
98.4%

Length

2025-07-17T22:28:17.617634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T22:28:17.878270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 3048
69.4%
1 1121
 
25.5%
3 221
 
5.0%

Most occurring characters

ValueCountFrequency (%)
2 3048
69.4%
1 1121
 
25.5%
3 221
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3048
69.4%
1 1121
 
25.5%
3 221
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3048
69.4%
1 1121
 
25.5%
3 221
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3048
69.4%
1 1121
 
25.5%
3 221
 
5.0%

RiskSafetyBelt
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.1%
Missing278074
Missing (%)99.3%
Memory size19.2 MiB
2
1363 
3
625 
1
 
78

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2,066
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 1363
 
0.5%
3 625
 
0.2%
1 78
 
< 0.1%
(Missing) 278074
99.3%

Length

2025-07-17T22:28:18.145345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T22:28:18.426212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 1363
66.0%
3 625
30.3%
1 78
 
3.8%

Most occurring characters

ValueCountFrequency (%)
2 1363
66.0%
3 625
30.3%
1 78
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2066
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1363
66.0%
3 625
30.3%
1 78
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2066
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1363
66.0%
3 625
30.3%
1 78
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2066
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1363
66.0%
3 625
30.3%
1 78
 
3.8%
Distinct5204
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
Minimum2011-01-01 00:00:00
Maximum2025-03-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-17T22:28:18.777112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:28:19.271762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DateRec
Text

Missing 

Distinct4018
Distinct (%)3.8%
Missing173190
Missing (%)61.8%
Memory size14.2 MiB
2025-07-17T22:28:20.207394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.9682842
Min length8

Characters and Unicode

Total characters959,158
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1/1/2554
2nd row1/1/2554
3rd row1/1/2554
4th row1/1/2554
5th row1/1/2554
ValueCountFrequency (%)
1/1/2562 74
 
0.1%
4/13/2554 68
 
0.1%
1/1/2554 65
 
0.1%
12/29/2561 64
 
0.1%
4/13/2560 63
 
0.1%
1/1/2560 62
 
0.1%
31/12/2564 61
 
0.1%
4/13/2555 61
 
0.1%
4/13/2559 59
 
0.1%
1/1/2557 59
 
0.1%
Other values (4008) 106314
99.4%
2025-07-17T22:28:21.508790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 213716
22.3%
5 195020
20.3%
2 182015
19.0%
1 106485
11.1%
6 76624
 
8.0%
4 38437
 
4.0%
3 35185
 
3.7%
0 28954
 
3.0%
7 28101
 
2.9%
9 27658
 
2.9%
Other values (2) 26963
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 959158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 213716
22.3%
5 195020
20.3%
2 182015
19.0%
1 106485
11.1%
6 76624
 
8.0%
4 38437
 
4.0%
3 35185
 
3.7%
0 28954
 
3.0%
7 28101
 
2.9%
9 27658
 
2.9%
Other values (2) 26963
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 959158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 213716
22.3%
5 195020
20.3%
2 182015
19.0%
1 106485
11.1%
6 76624
 
8.0%
4 38437
 
4.0%
3 35185
 
3.7%
0 28954
 
3.0%
7 28101
 
2.9%
9 27658
 
2.9%
Other values (2) 26963
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 959158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 213716
22.3%
5 195020
20.3%
2 182015
19.0%
1 106485
11.1%
6 76624
 
8.0%
4 38437
 
4.0%
3 35185
 
3.7%
0 28954
 
3.0%
7 28101
 
2.9%
9 27658
 
2.9%
Other values (2) 26963
 
2.8%

TimeRec
Date

Missing 

Distinct1440
Distinct (%)1.3%
Missing173190
Missing (%)61.8%
Memory size4.3 MiB
Minimum1900-01-01 00:00:00
Maximum1900-01-01 23:59:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-17T22:28:21.880115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:28:22.471106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AccSubDist
Categorical

High cardinality  Missing 

Distinct5967
Distinct (%)4.6%
Missing150422
Missing (%)53.7%
Memory size3.3 MiB
ในเมือง
 
1919
หนองปรือ
 
636
หนองบัว
 
457
บางเสาธง
 
352
ท่าข้าม
 
340
Other values (5962)
126014 

Length

Max length20
Median length18
Mean length7.3930141
Min length2

Characters and Unicode

Total characters959,007
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique229 ?
Unique (%)0.2%

Sample

1st rowเขาพระงาม
2nd rowในเมือง
3rd rowนาแก
4th rowสันทรายน้อย
5th rowบ้านมะเกลือ

Common Values

ValueCountFrequency (%)
ในเมือง 1919
 
0.7%
หนองปรือ 636
 
0.2%
หนองบัว 457
 
0.2%
บางเสาธง 352
 
0.1%
ท่าข้าม 340
 
0.1%
หนองขาม 333
 
0.1%
ทุ่งสุขลา 324
 
0.1%
คลองหนึ่ง 318
 
0.1%
เวียง 313
 
0.1%
ท่าช้าง 305
 
0.1%
Other values (5957) 124421
44.4%
(Missing) 150422
53.7%

Length

2025-07-17T22:28:22.940686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ในเมือง 1919
 
1.5%
หนองปรือ 636
 
0.5%
หนองบัว 457
 
0.4%
บางเสาธง 352
 
0.3%
ท่าข้าม 340
 
0.3%
หนองขาม 333
 
0.3%
ทุ่งสุขลา 324
 
0.2%
คลองหนึ่ง 318
 
0.2%
เวียง 313
 
0.2%
ท่าช้าง 305
 
0.2%
Other values (5957) 124424
95.9%

Most occurring characters

ValueCountFrequency (%)
86633
 
9.0%
73970
 
7.7%
67159
 
7.0%
48207
 
5.0%
36323
 
3.8%
34510
 
3.6%
34164
 
3.6%
33086
 
3.5%
32412
 
3.4%
31110
 
3.2%
Other values (59) 481433
50.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 959007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
86633
 
9.0%
73970
 
7.7%
67159
 
7.0%
48207
 
5.0%
36323
 
3.8%
34510
 
3.6%
34164
 
3.6%
33086
 
3.5%
32412
 
3.4%
31110
 
3.2%
Other values (59) 481433
50.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 959007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
86633
 
9.0%
73970
 
7.7%
67159
 
7.0%
48207
 
5.0%
36323
 
3.8%
34510
 
3.6%
34164
 
3.6%
33086
 
3.5%
32412
 
3.4%
31110
 
3.2%
Other values (59) 481433
50.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 959007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
86633
 
9.0%
73970
 
7.7%
67159
 
7.0%
48207
 
5.0%
36323
 
3.8%
34510
 
3.6%
34164
 
3.6%
33086
 
3.5%
32412
 
3.4%
31110
 
3.2%
Other values (59) 481433
50.2%

AccDistrict
Categorical

High cardinality  Missing 

Distinct1117
Distinct (%)0.8%
Missing137688
Missing (%)49.1%
Memory size2.8 MiB
ศรีราชา
 
1531
เมืองนครราชสีมา
 
1386
เมืองระยอง
 
1312
บางละมุง
 
1193
เมืองชลบุรี
 
1157
Other values (1112)
135873 

Length

Max length24
Median length20
Mean length8.9190113
Min length1

Characters and Unicode

Total characters1,270,531
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowเมืองลพบุรี
2nd rowมีนบุรี
3rd rowหนองสูง
4th rowเมืองนครราชสีมา
5th rowนาแก

Common Values

ValueCountFrequency (%)
ศรีราชา 1531
 
0.5%
เมืองนครราชสีมา 1386
 
0.5%
เมืองระยอง 1312
 
0.5%
บางละมุง 1193
 
0.4%
เมืองชลบุรี 1157
 
0.4%
เมืองขอนแก่น 1140
 
0.4%
เมืองเชียงราย 1125
 
0.4%
เมืองเชียงใหม่ 1038
 
0.4%
เมืองอุดรธานี 1031
 
0.4%
เมืองสมุทรปราการ 1013
 
0.4%
Other values (1107) 130526
46.6%
(Missing) 137688
49.1%

Length

2025-07-17T22:28:23.374506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ศรีราชา 1531
 
1.1%
เมืองนครราชสีมา 1386
 
1.0%
เมืองระยอง 1312
 
0.9%
บางละมุง 1193
 
0.8%
เมืองชลบุรี 1157
 
0.8%
เมืองขอนแก่น 1140
 
0.8%
เมืองเชียงราย 1125
 
0.8%
เมืองเชียงใหม่ 1038
 
0.7%
เมืองอุดรธานี 1031
 
0.7%
เมืองสมุทรปราการ 1013
 
0.7%
Other values (1102) 131065
91.7%

Most occurring characters

ValueCountFrequency (%)
102376
 
8.1%
92159
 
7.3%
83778
 
6.6%
76358
 
6.0%
75228
 
5.9%
66491
 
5.2%
66473
 
5.2%
42051
 
3.3%
36616
 
2.9%
35201
 
2.8%
Other values (56) 593800
46.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1270531
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
102376
 
8.1%
92159
 
7.3%
83778
 
6.6%
76358
 
6.0%
75228
 
5.9%
66491
 
5.2%
66473
 
5.2%
42051
 
3.3%
36616
 
2.9%
35201
 
2.8%
Other values (56) 593800
46.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1270531
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
102376
 
8.1%
92159
 
7.3%
83778
 
6.6%
76358
 
6.0%
75228
 
5.9%
66491
 
5.2%
66473
 
5.2%
42051
 
3.3%
36616
 
2.9%
35201
 
2.8%
Other values (56) 593800
46.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1270531
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
102376
 
8.1%
92159
 
7.3%
83778
 
6.6%
76358
 
6.0%
75228
 
5.9%
66491
 
5.2%
66473
 
5.2%
42051
 
3.3%
36616
 
2.9%
35201
 
2.8%
Other values (56) 593800
46.7%

AccProvince
Categorical

High cardinality  High correlation 

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
นครราชสีมา
 
12613
กรุงเทพมหานคร
 
11649
ชลบุรี
 
10681
เชียงใหม่
 
8570
อุบลราชธานี
 
8079
Other values (73)
228548 

Length

Max length15
Median length12
Mean length8.1842543
Min length3

Characters and Unicode

Total characters2,292,737
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowกำแพงเพชร
2nd rowลพบุรี
3rd rowอุดรธานี
4th rowนครสวรรค์
5th rowชลบุรี

Common Values

ValueCountFrequency (%)
นครราชสีมา 12613
 
4.5%
กรุงเทพมหานคร 11649
 
4.2%
ชลบุรี 10681
 
3.8%
เชียงใหม่ 8570
 
3.1%
อุบลราชธานี 8079
 
2.9%
ขอนแก่น 7341
 
2.6%
เชียงราย 6685
 
2.4%
ระยอง 6552
 
2.3%
นครศรีธรรมราช 6108
 
2.2%
สงขลา 5951
 
2.1%
Other values (68) 195911
69.9%

Length

2025-07-17T22:28:23.913538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
นครราชสีมา 12613
 
4.5%
กรุงเทพมหานคร 11649
 
4.2%
ชลบุรี 10681
 
3.8%
เชียงใหม่ 8570
 
3.1%
อุบลราชธานี 8079
 
2.9%
ขอนแก่น 7341
 
2.6%
เชียงราย 6685
 
2.4%
ระยอง 6552
 
2.3%
นครศรีธรรมราช 6108
 
2.2%
สงขลา 5951
 
2.1%
Other values (68) 195911
69.9%

Most occurring characters

ValueCountFrequency (%)
335185
 
14.6%
170631
 
7.4%
143524
 
6.3%
134162
 
5.9%
130321
 
5.7%
81683
 
3.6%
80614
 
3.5%
80522
 
3.5%
73843
 
3.2%
71414
 
3.1%
Other values (41) 990838
43.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2292737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
335185
 
14.6%
170631
 
7.4%
143524
 
6.3%
134162
 
5.9%
130321
 
5.7%
81683
 
3.6%
80614
 
3.5%
80522
 
3.5%
73843
 
3.2%
71414
 
3.1%
Other values (41) 990838
43.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2292737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
335185
 
14.6%
170631
 
7.4%
143524
 
6.3%
134162
 
5.9%
130321
 
5.7%
81683
 
3.6%
80614
 
3.5%
80522
 
3.5%
73843
 
3.2%
71414
 
3.1%
Other values (41) 990838
43.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2292737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
335185
 
14.6%
170631
 
7.4%
143524
 
6.3%
134162
 
5.9%
130321
 
5.7%
81683
 
3.6%
80614
 
3.5%
80522
 
3.5%
73843
 
3.2%
71414
 
3.1%
Other values (41) 990838
43.2%

AccLat
Real number (ℝ)

Missing 

Distinct115751
Distinct (%)92.0%
Missing154275
Missing (%)55.1%
Infinite0
Infinite (%)0.0%
Mean17.682844
Minimum-74.389096
Maximum105.57089
Zeros135
Zeros (%)< 0.1%
Negative5
Negative (%)< 0.1%
Memory size4.3 MiB
2025-07-17T22:28:24.301286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.389096
5-th percentile7.7771842
Q113.63479
median14.77377
Q316.683809
95-th percentile19.889452
Maximum105.57089
Range179.95998
Interquartile range (IQR)3.049019

Descriptive statistics

Standard deviation16.543492
Coefficient of variation (CV)0.93556738
Kurtosis20.67808
Mean17.682844
Median Absolute Deviation (MAD)1.452864
Skewness4.6617381
Sum2225651.1
Variance273.68712
MonotonicityNot monotonic
2025-07-17T22:28:24.821952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
 
< 0.1%
13.7246005 55
 
< 0.1%
13.761728 22
 
< 0.1%
13.591977 17
 
< 0.1%
14.888914 15
 
< 0.1%
14.82525 15
 
< 0.1%
13.258378 14
 
< 0.1%
17.400572 13
 
< 0.1%
14.55217 13
 
< 0.1%
12.270556 12
 
< 0.1%
Other values (115741) 125554
44.8%
(Missing) 154275
55.1%
ValueCountFrequency (%)
-74.3890963 1
 
< 0.1%
-19.52967558 1
 
< 0.1%
-5.779257 1
 
< 0.1%
-0.076517701 1
 
< 0.1%
-0.026961521 1
 
< 0.1%
0 135
< 0.1%
0.000815 1
 
< 0.1%
0.257533413 1
 
< 0.1%
5.728719 1
 
< 0.1%
5.736054051 1
 
< 0.1%
ValueCountFrequency (%)
105.570887 1
< 0.1%
105.477108 1
< 0.1%
105.459953 1
< 0.1%
105.452247 1
< 0.1%
105.412665 1
< 0.1%
105.396558 1
< 0.1%
105.362885 1
< 0.1%
105.357029 1
< 0.1%
105.348246 1
< 0.1%
105.328907 1
< 0.1%

AccLong
Real number (ℝ)

High correlation  Missing 

Distinct114211
Distinct (%)90.7%
Missing154276
Missing (%)55.1%
Infinite0
Infinite (%)0.0%
Mean97.83643
Minimum-92.511648
Maximum105.59982
Zeros135
Zeros (%)< 0.1%
Negative4
Negative (%)< 0.1%
Memory size4.3 MiB
2025-07-17T22:28:25.316704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-92.511648
5-th percentile98.379335
Q199.908817
median100.63798
Q3102.08213
95-th percentile104.33246
Maximum105.59982
Range198.11147
Interquartile range (IQR)2.1733158

Descriptive statistics

Standard deviation16.685078
Coefficient of variation (CV)0.17054054
Kurtosis21.3107
Mean97.83643
Median Absolute Deviation (MAD)0.89550106
Skewness-4.7909909
Sum12314084
Variance278.39182
MonotonicityNot monotonic
2025-07-17T22:28:25.905356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
 
< 0.1%
100.6331108 55
 
< 0.1%
100.65279 22
 
< 0.1%
101.762223 17
 
< 0.1%
100.818269 15
 
< 0.1%
101.546339 14
 
< 0.1%
101.200573 14
 
< 0.1%
100.966877 14
 
< 0.1%
100.614025 14
 
< 0.1%
100.645634 14
 
< 0.1%
Other values (114201) 125550
44.8%
(Missing) 154276
55.1%
ValueCountFrequency (%)
-92.51164801 1
 
< 0.1%
-35.200946 1
 
< 0.1%
-0.022865615 1
 
< 0.1%
-0.009362376 1
 
< 0.1%
0 135
< 0.1%
0.008755 1
 
< 0.1%
0.033774711 1
 
< 0.1%
3.409616053 1
 
< 0.1%
5.942709 1
 
< 0.1%
6.025122 1
 
< 0.1%
ValueCountFrequency (%)
105.599818 1
< 0.1%
105.573204 1
< 0.1%
105.572732 1
< 0.1%
105.569599 1
< 0.1%
105.569427 1
< 0.1%
105.563333 1
< 0.1%
105.562346 1
< 0.1%
105.556925 1
< 0.1%
105.555282 1
< 0.1%
105.544848 1
< 0.1%

ICD_10
Categorical

High cardinality  Imbalance  Missing 

Distinct375
Distinct (%)0.2%
Missing59503
Missing (%)21.2%
Memory size2.7 MiB
V892
104650 
V299
26689 
Y349
18904 
V499
 
8427
V234
 
7734
Other values (370)
54233 

Length

Max length4
Median length4
Mean length3.9998912
Min length3

Characters and Unicode

Total characters882,524
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)< 0.1%

Sample

1st rowV499
2nd rowV499
3rd rowV899
4th rowV239
5th rowV892

Common Values

ValueCountFrequency (%)
V892 104650
37.4%
V299 26689
 
9.5%
Y349 18904
 
6.7%
V499 8427
 
3.0%
V234 7734
 
2.8%
V284 5424
 
1.9%
V289 3676
 
1.3%
V239 3563
 
1.3%
X599 2960
 
1.1%
V899 2724
 
1.0%
Other values (365) 35886
 
12.8%
(Missing) 59503
21.2%

Length

2025-07-17T22:28:26.332888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v892 104652
47.4%
v299 26690
 
12.1%
y349 18904
 
8.6%
v499 8427
 
3.8%
v234 7734
 
3.5%
v284 5425
 
2.5%
v289 3676
 
1.7%
v239 3563
 
1.6%
x599 2960
 
1.3%
v899 2724
 
1.2%
Other values (359) 35882
 
16.3%

Most occurring characters

ValueCountFrequency (%)
9 229978
26.1%
V 198684
22.5%
2 172811
19.6%
8 121605
13.8%
4 62332
 
7.1%
3 36529
 
4.1%
Y 18973
 
2.1%
5 11477
 
1.3%
0 9164
 
1.0%
1 8175
 
0.9%
Other values (4) 12796
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 882524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 229978
26.1%
V 198684
22.5%
2 172811
19.6%
8 121605
13.8%
4 62332
 
7.1%
3 36529
 
4.1%
Y 18973
 
2.1%
5 11477
 
1.3%
0 9164
 
1.0%
1 8175
 
0.9%
Other values (4) 12796
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 882524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 229978
26.1%
V 198684
22.5%
2 172811
19.6%
8 121605
13.8%
4 62332
 
7.1%
3 36529
 
4.1%
Y 18973
 
2.1%
5 11477
 
1.3%
0 9164
 
1.0%
1 8175
 
0.9%
Other values (4) 12796
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 882524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 229978
26.1%
V 198684
22.5%
2 172811
19.6%
8 121605
13.8%
4 62332
 
7.1%
3 36529
 
4.1%
Y 18973
 
2.1%
5 11477
 
1.3%
0 9164
 
1.0%
1 8175
 
0.9%
Other values (4) 12796
 
1.4%

Vehicle
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
ไม่ระบุพาหนะ
129689 
รถจักรยานยนต์
114292 
รถยนต์
19743 
คนเดินเท้า
 
8312
รถบรรทุกขนาดเล็ก/รถตู้
 
4648
Other values (5)
 
3456

Length

Max length22
Median length15
Mean length12.063001
Min length6

Characters and Unicode

Total characters3,379,329
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowรถยนต์
2nd rowรถยนต์
3rd rowไม่ระบุพาหนะ
4th rowไม่ระบุพาหนะ
5th rowไม่ระบุพาหนะ

Common Values

ValueCountFrequency (%)
ไม่ระบุพาหนะ 129689
46.3%
รถจักรยานยนต์ 114292
40.8%
รถยนต์ 19743
 
7.0%
คนเดินเท้า 8312
 
3.0%
รถบรรทุกขนาดเล็ก/รถตู้ 4648
 
1.7%
รถจักรยาน 1458
 
0.5%
รถบรรทุกหนัก 1045
 
0.4%
สามล้อ 463
 
0.2%
รถโดยสาร 337
 
0.1%
รถเพื่อการเกษตร 153
 
0.1%

Length

2025-07-17T22:28:26.746364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-17T22:28:27.129571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ไม่ระบุพาหนะ 129689
46.3%
รถจักรยานยนต์ 114292
40.8%
รถยนต์ 19743
 
7.0%
คนเดินเท้า 8312
 
3.0%
รถบรรทุกขนาดเล็ก/รถตู้ 4648
 
1.7%
รถจักรยาน 1458
 
0.5%
รถบรรทุกหนัก 1045
 
0.4%
สามล้อ 463
 
0.2%
รถโดยสาร 337
 
0.1%
รถเพื่อการเกษตร 153
 
0.1%

Most occurring characters

ValueCountFrequency (%)
403792
 
11.9%
401791
 
11.9%
259378
 
7.7%
259352
 
7.7%
250122
 
7.4%
146324
 
4.3%
138836
 
4.1%
135382
 
4.0%
135382
 
4.0%
134035
 
4.0%
Other values (24) 1114935
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3379329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
403792
 
11.9%
401791
 
11.9%
259378
 
7.7%
259352
 
7.7%
250122
 
7.4%
146324
 
4.3%
138836
 
4.1%
135382
 
4.0%
135382
 
4.0%
134035
 
4.0%
Other values (24) 1114935
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3379329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
403792
 
11.9%
401791
 
11.9%
259378
 
7.7%
259352
 
7.7%
250122
 
7.4%
146324
 
4.3%
138836
 
4.1%
135382
 
4.0%
135382
 
4.0%
134035
 
4.0%
Other values (24) 1114935
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3379329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
403792
 
11.9%
401791
 
11.9%
259378
 
7.7%
259352
 
7.7%
250122
 
7.4%
146324
 
4.3%
138836
 
4.1%
135382
 
4.0%
135382
 
4.0%
134035
 
4.0%
Other values (24) 1114935
33.0%

Interactions

2025-07-17T22:28:00.810563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:38.379819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:41.445145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:46.814889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:50.756091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:57.018475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:28:01.275591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:39.050844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:42.313077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:47.229965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:51.950502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:57.647336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:28:02.050459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:39.502513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:42.868993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:47.683856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:53.326913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:58.255080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:28:02.462570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:40.007049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:44.570158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:48.294783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:54.541142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:59.153808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:28:02.913007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:40.389125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:45.252977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:48.923433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:55.302294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:59.824727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:28:03.441148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:40.838491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:46.096894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:49.607070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:27:56.155166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-17T22:28:00.377518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-17T22:28:27.535211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AccLatAccLongAccProvinceAgeDeadYear_ADDeadYear_BERiskHelmetRiskSafetyBeltSexVehicleid
AccLat1.0000.0310.4710.0290.1160.1160.1410.1510.0300.0620.087
AccLong0.0311.0000.055-0.039-0.104-0.1040.0001.0000.0280.059-0.082
AccProvince0.4710.0551.0000.0590.0280.0280.2970.5220.0560.0710.035
Age0.029-0.0390.0591.0000.0910.0910.0610.0590.0740.0560.083
DeadYear_AD0.116-0.1040.0280.0911.0001.0000.2340.0290.1070.0960.903
DeadYear_BE0.116-0.1040.0280.0911.0001.0000.2340.0290.1070.0960.903
RiskHelmet0.1410.0000.2970.0610.2340.2341.0000.2370.0210.0651.000
RiskSafetyBelt0.1511.0000.5220.0590.0290.0290.2371.0000.0360.0271.000
Sex0.0300.0280.0560.0740.1070.1070.0210.0361.0000.0720.093
Vehicle0.0620.0590.0710.0560.0960.0960.0650.0270.0721.0000.136
id0.087-0.0820.0350.0830.9030.9031.0001.0000.0930.1361.000

Missing values

2025-07-17T22:28:04.346098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-17T22:28:06.231200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-17T22:28:09.296922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idDeadYear_BEDeadYear_ADAgeSexNationalitySubDistDistrictProvinceRiskHelmetRiskSafetyBeltDeadDateDateRecTimeRecAccSubDistAccDistrictAccProvinceAccLatAccLongICD_10Vehicle
086008192554201138ชาย99.0NaNNaNNaN<NA><NA>1/1/2011NaNNaNNaNNaNกำแพงเพชรNaNNaNV499รถยนต์
186008232554201141ชาย99.0NaNNaNNaN<NA><NA>1/1/2011NaNNaNNaNNaNลพบุรีNaNNaNV499รถยนต์
286008222554201154ชาย99.0NaNNaNNaN<NA><NA>1/1/2011NaNNaNNaNNaNอุดรธานีNaNNaNV899ไม่ระบุพาหนะ
3861077825542011<NA>ไม่ระบุNaNNaNNaNNaN<NA><NA>1/1/2011NaNNaNNaNNaNนครสวรรค์NaNNaNNaNไม่ระบุพาหนะ
4860893825542011<NA>ไม่ระบุNaNNaNNaNNaN<NA><NA>1/1/2011NaNNaNNaNNaNชลบุรีNaNNaNNaNไม่ระบุพาหนะ
5860673225542011<NA>ชายNaNเขาพระงามเมืองลพบุรีลบ<NA><NA>1/1/20111/1/25541/1/1900 22:30เขาพระงามเมืองลพบุรีลพบุรี14.903639100.590269NaNไม่ระบุพาหนะ
6860722325542011<NA>ชายNaNNaNNaNNaN<NA><NA>1/1/20111/1/25541/1/1900 22:30NaNมีนบุรีกรุงเทพมหานคร13.812936100.731690NaNไม่ระบุพาหนะ
7860479725542011<NA>ชายNaNบ้านเป้าหนองสูงมห<NA><NA>1/1/20111/1/25541/1/1900 20:08NaNหนองสูงมุกดาหาร16.419464104.340423NaNไม่ระบุพาหนะ
886008202554201179ชาย99.0NaNNaNNaN<NA><NA>1/1/2011NaNNaNNaNNaNปราจีนบุรีNaNNaNV239รถจักรยานยนต์
986008212554201142ชาย99.0NaNNaNNaN<NA><NA>1/1/2011NaNNaNNaNNaNศรีสะเกษNaNNaNV892ไม่ระบุพาหนะ
idDeadYear_BEDeadYear_ADAgeSexNationalitySubDistDistrictProvinceRiskHelmetRiskSafetyBeltDeadDateDateRecTimeRecAccSubDistAccDistrictAccProvinceAccLatAccLongICD_10Vehicle
28013030015982568202574หญิงNaNNaNNaNNaN<NA><NA>2025-02-15NaNNaNNaNเมืองสมุทรปราการสมุทรปราการNaNNaNV892ไม่ระบุพาหนะ
28013130018542568202560หญิงNaNNaNNaNNaN<NA><NA>2025-02-16NaNNaNNaNเมืองระยองระยองNaNNaNV892ไม่ระบุพาหนะ
28013230031342568202550ชายNaNNaNNaNNaN<NA><NA>2025-02-28NaNNaNNaNเมืองกาฬสินธุ์กาฬสินธุ์NaNNaNV892ไม่ระบุพาหนะ
28013330036462568202551ชายNaNNaNNaNNaN<NA><NA>2025-03-01NaNNaNNaNเมืองฉะเชิงเทราฉะเชิงเทราNaNNaNV892ไม่ระบุพาหนะ
28013430041582568202557หญิงNaNNaNNaNNaN<NA><NA>2025-03-03NaNNaNNaNเมืองร้อยเอ็ดร้อยเอ็ดNaNNaNV274รถจักรยานยนต์
28013530003192568202523ชายNaNNaNNaNNaN<NA><NA>2025-01-21NaNNaNNaNหนองบัวระเหวชัยภูมิNaNNaNV892ไม่ระบุพาหนะ
28013630018552568202557ชายNaNNaNNaNNaN<NA><NA>2025-02-17NaNNaNNaNเมืองระยองระยองNaNNaNV892ไม่ระบุพาหนะ
28013730033912568202558หญิงNaNNaNNaNNaN<NA><NA>2025-03-02NaNNaNNaNเมืองปทุมธานีปทุมธานีNaNNaNV892ไม่ระบุพาหนะ
28013830041592568202565ชายNaNNaNNaNNaN<NA><NA>2025-03-02NaNNaNNaNเมืองร้อยเอ็ดร้อยเอ็ดNaNNaNV234รถจักรยานยนต์
28013930049272568202568หญิงNaNNaNNaNNaN<NA><NA>2025-03-29NaNNaNNaNเมืองสุราษฎร์ธานีสุราษฎร์ธานีNaNNaNV892ไม่ระบุพาหนะ